PRISM samples
Output
load_xls
Log text:
loaded assay from Excel file ‘Metabolome_data.xlsx, sheet ’Metabolite_abundance_PRISM’
anno_xls
Log text:
loaded features annotations from Excel file ‘Metabolome_data.xlsx, sheet ’Metabolite_features’
anno_xls
Log text:
loaded samples annotations from Excel file ‘Metabolome_data.xlsx, sheet ’Metabolome_metadata_PRISM’
reporting_data
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Dataset info: 155 samples, 8848 features; 9 sample annotation fields, 8 feature annotation fields
pre_zero_to_na
Log text:
zeros replaced by NAs
Missingness analysis
Perform missingness analysis to determine if NAs significantly accumulate in one of the treatment groups. Adjust output of test using multiple testing correction.
stats_univ_missingness
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missingness analysis with variable Diagnosis
WARNING: Large data frame (3829 rows). Displaying first 1000 rows.
plots_pval_qq
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P-value QQ plot for missingness
post_multtest
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Multiple testing correction of ‘missingness’ using ‘BH’
Normalization
Plot sample boxplots before normalization, perform quotient normalization, plot boxplot with dilution factors from quotient normalization, plot sample boxplot after normalization, log transform the data, impute missing data using min value, plot sample boxplot after imputation, detect outliers, log dataset info, write pre-processed data to file.
plots_sample_boxplot
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sample boxplot, aes: color=Diagnosis
pre_norm_quot
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quotient normalization based on 34 reference samples and 1799 variables: ~
Log text:
quotient normalization based on 34 reference samples and 1799 variables: Diagnosis == “Control”
plots_dilution_factor
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dilution factor plot, ‘Diagnosis’
plots_sample_boxplot
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sample boxplot, aes: color=Diagnosis
pre_trans_log
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log2.000000
pre_impute_min
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imputed via minimum value, 0 features with all NAs, returned as NAs
plots_sample_boxplot
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sample boxplot, aes: color=Diagnosis
pre_outlier_detection_univariate
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flagged 0 univariate outliers
reporting_data
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Dataset info: 155 samples, 3829 features; 11 sample annotation fields, 8 feature annotation fields
modify_filter_samples
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Filter samples: ~
Log text:
Filter samples: !is.na(Diagnosis)
anno_apply
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Transformed column ‘Diagnosis’ of sample annotations
plots_missingness
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missingness plots, missing values: 0 out of 593495 (0.00%)
pre_filter_missingness
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features filtered, 50.00%, 0 of 3829 removed
plots_missingness
Log text:
missingness plots, missing values: 0 out of 593495 (0.00%)